Overview

Embedding-Based Retrieval in Action: Real-World AI Applications with Semantic Search and RAG 2.0 for RAG Workflows, and Vector Databases What if your AI systems could understand meaning instead of just matching keywords? In an era where large language models are powerful but often unreliable, embedding-based retrieval has become the critical bridge to accuracy, scalability, and trust. This book provides a practical, hands-on guide to building retrieval systems powered by embeddings, semantic search, and RAG 2.0. Designed for developers, data scientists, and AI professionals, it shows how to move from theory to real-world applications-whether you are building smarter chatbots, enterprise knowledge assistants, or recommendation engines. Readers will gain the tools and strategies to design retrieval pipelines, work with vector databases, and deploy production-ready RAG workflows that actually deliver reliable results. What makes this book different is its end-to-end approach. It not only explains the foundations of embeddings and semantic search but also demonstrates how to put them in action with extensive code examples and system architectures. Key chapters include: Foundations of Embeddings and Retrieval - how AI moved from keyword search to dense vectors. Semantic Search Explained - principles, limitations of traditional search, and similarity metrics. Introduction to RAG and RAG 2.0 - why grounding matters and how RAG 2.0 enhances reliability. Working with Vector Databases - Pinecone, Weaviate, Milvus, FAISS, schema design, and performance. Building Retrieval Pipelines - ingestion, querying, ranking, and evaluating quality. Embeddings in Practice - generating, fine-tuning, and balancing pretrained vs. custom models. RAG 2.0 in Real Applications - chatbots, enterprise assistants, and recommendation systems. Scaling and Deployment - distributed systems, Kubernetes, serverless strategies, and cost optimization. Evaluation, Security, and Compliance - benchmarks, reducing hallucinations, explainability, and privacy. Future Directions - multimodal retrieval, emerging trends in vector databases, and the role of embeddings in agentic AI. Each chapter blends conceptual depth with actionable practice, ensuring readers not only understand why these systems matter but also how to build and scale them effectively. If you want to create AI applications that are accurate, context-aware, and production-ready, this book is your guide. Take the next step toward mastering embedding-based retrieval and build systems that your users can truly rely on.

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